Abstract: A method for loading data into a datamart. The method includes identifying business data in a first format from a real business process and identifying a desired second format. The method includes designing a transformation algorithm that transforms the business data in the first format into the second format and implementing the transformation algorithm in computer executable code. The method includes running the computer executable code on business data in the first format and generating business data in the second format and storing the business data in the second format in a datamart.

Abstract: An elastomeric load bearing surface with different load support characteristics in different directions. In one embodiment, the surface includes an elastomeric membrane that is oriented in only a single direction, for example, by compression or stretching. In another embodiment, the surface includes mechanical structures, such as connectors, variations in thickness and apertures, that vary the load support characteristics in different directions. In another aspect, the present invention provides a multilayer load bearing surface in which the layers cooperate to provide a controlled force/deflection profile that is variable in different regions of the surface. In one embodiment, the upper layer includes a plurality of loosely connected nodes and a lower layer having a plurality of resilient elements that separately support each node.

Abstract: A method for loading data into a datamart. The method includes identifying business data in a first format from a real business process and identifying a desired second format. The method includes designing a transformation algorithm that transforms the business data in the first format into the second format and implementing the transformation algorithm in computer executable code. The method includes running the computer executable code on business data in the first format and generating business data in the second format and storing the business data in the second format in a datamart.

Abstract: A method, system, and computer program product visualizes the structure of an evidence classifier. An evidence inducer generates an evidence classifier based on a training set of labeled records. A mapping module generates visualization data files. An evidence visualization tool uses the visualization data files to display an evidence pane and/or a label probability pane. A first evidence pane display view shows a normalized conditional probability of each label value, for each attribute value. The first evidence pane display view can be a plurality of rows of pie charts. Each pie slice in a pie chart has a size which is a function of the normalized conditional probability of each label value for the respective attribute value. For each pie chart, the mapping module maps a height that is a function of the number of records in the training set associated with the evidence classifier.

Abstract: A system and method determines how well various attributes in a record discriminate different values of a chosen label attribute. An attribute is considered a relevant attribute if it discriminates different values of a chosen label attribute either alone or in conjunction with other attributes. According to the present invention, a label attribute is selected by a user from a set of records, with each record having a plurality of attributes. Next, one or more first important attributes considered important by the user are selected. The present invention then generates one or more second important attributes. The second important attributes together with the user chosen first important attributes discriminate well between different values of the label attribute. A measure called "purity" (a number from 0 to 100) informs how well each attribute discriminates the different label attributes. The purity measure allows the attributes to be ranked based on their importance.

Abstract: A method, system, and computer program product visualizes the structure of an evidence classifier. An evidence inducer generates an evidence classifier based on a training set of labeled records. A mapping module generates visualization data files. An evidence visualization tool uses the visualization data files to display an evidence pane and/or a label probability pane. A first evidence pane display view shows a normalized conditional probability of each label value, for each attribute value. The first evidence pane display view can be a plurality of rows of pie charts. Each pie slice in a pie chart has a size which is a function of the normalized conditional probability of each label value for the respective attribute value. A second evidence pane display view shows relative conditional probabilities of a selected label value, for each attribute value. The second evidence pane display view can be a plurality of rows of bars.